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Lambert TP, Chan M, Sanchez-Perez JA, Nikbakht M, Lin DJ, Nawar A, Bashar SK, Kimball JP, Zia JS, Gazi AH, Cestero GI, Corporan D, Padala M, Hahn JO, Inan OT. A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model. BIOSENSORS 2024; 14:61. [PMID: 38391980 PMCID: PMC10886994 DOI: 10.3390/bios14020061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/07/2024] [Accepted: 01/16/2024] [Indexed: 02/24/2024]
Abstract
Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10-3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10-3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.
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Affiliation(s)
- Tamara P. Lambert
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (O.T.I.)
| | - Michael Chan
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (O.T.I.)
| | - Jesus Antonio Sanchez-Perez
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Mohammad Nikbakht
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - David J. Lin
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Afra Nawar
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Syed Khairul Bashar
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Jacob P. Kimball
- The Donald P. Shiley School of Engineering, University of Portland, Portland, OR 97203, USA;
| | - Jonathan S. Zia
- Division of Neurology & Neurological Sciences, Stanford School of Medicine, Palo Alto, CA 94304, USA;
| | - Asim H. Gazi
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134, USA;
| | - Gabriela I. Cestero
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Daniella Corporan
- Structural Heart Research and Innovation Laboratory, Carlyle Fraser Heart Center, Emory University Hospital Midtown, Atlanta, GA 30308, USA; (D.C.); (M.P.)
- Division of Cardiothoracic Surgery, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Muralidhar Padala
- Structural Heart Research and Innovation Laboratory, Carlyle Fraser Heart Center, Emory University Hospital Midtown, Atlanta, GA 30308, USA; (D.C.); (M.P.)
- Division of Cardiothoracic Surgery, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA;
| | - Omer T. Inan
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (O.T.I.)
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
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Bedolla CN, Gonzalez JM, Vega SJ, Convertino VA, Snider EJ. An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability. Bioengineering (Basel) 2023; 10:bioengineering10050612. [PMID: 37237682 DOI: 10.3390/bioengineering10050612] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/08/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient's status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.
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Affiliation(s)
- Carlos N Bedolla
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Jose M Gonzalez
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Saul J Vega
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Víctor A Convertino
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- Department of Medicine, Uniformed Services University, Bethesda, MD 20814, USA
- Department of Emergency Medicine, University of Texas Health, San Antonio, TX 78229, USA
- Department of Biomedical Engineering, University of Texas Health, San Antonio, TX 78249, USA
| | - Eric J Snider
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
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Gupta JF, Arshad SH, Telfer BA, Snider EJ, Convertino VA. Noninvasive Monitoring of Simulated Hemorrhage and Whole Blood Resuscitation. BIOSENSORS 2022; 12:bios12121168. [PMID: 36551134 PMCID: PMC9775873 DOI: 10.3390/bios12121168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/03/2022] [Accepted: 12/08/2022] [Indexed: 06/01/2023]
Abstract
Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage and resuscitation. A gradient-boosted regression tree model trained on only the half-rise to dicrotic notch (HRDN) feature achieved a root-mean-square error (RMSE) of 13%, an R2 of 0.82, and area under the receiver operating characteristic curve of 0.97 for detecting decompensation. This single-feature model's performance compares favorably to previously reported results from more-complex black box machine learning models. This model further provides physiological insight because HRDN represents an approximate measure of the delay between the ABP ejected and reflected wave and therefore is an indication of cardiac and peripheral vascular mechanisms that contribute to the compensatory response to blood loss and replacement.
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Affiliation(s)
- Jay F. Gupta
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA
| | - Saaid H. Arshad
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA
| | - Brian A. Telfer
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA
| | - Eric J. Snider
- U.S. Army Institute of Surgical Research, San Antonio, TX 78234, USA
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